Análisis de las principales variables macroeconómicas que influyen en la exportación del oro en el Perú, periodo 2000 -2015
Bibliographic record
Abstract
An analysis of the main macroeconomic variables that influence gold exports in Peru, period 2000 - 2015. Likewise, it seeks to determine the elasticities of gold exports against the variations of the main macroeconomic variables that influence gold exports. The Pesaran cointegration econometric model was used. It was found that the elasticities of gold exports against the Gross Domestic Product are 5.04%, 0.82%, and 6.99% for Switzerland, Canada, and the United States, respectively. Furthermore, the terms of trade have had a significant impact on the central destination countries. For example, being cheerful and elastic for the Swiss and United States markets with 0.004% and 0.03% respectively, and negative for the Canadian market with -1.09%. These results can be used to improve the economic policies of international trade for the exporting companies of mining products in the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".